Abstract

Students’ performance in the continuous assessments needs to be monitored to identify the students who may not perform well in the final examination. The aim of the research is to predict the students at risk those who will not complete the course. In order to predict the students at risk, Machine Learning algorithms can be applied to the students’ data at hand to construct a model from the training data set. With the prediction model, testing data can be applied to identify the students at risk. In this paper, two Machine Learning Algorithms namely C4.5, and Naïve Bayes Algorithm are used to analyze the training data set to build the prediction models and tested on the testing data set. The accuracy level of the two algorithms were also computed and compared to identify the algorithm which yields results at higher accuracy.